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743 lines
30 KiB
743 lines
30 KiB
import argparse
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import hashlib
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import itertools
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import math
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import os
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import random
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from pathlib import Path
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from typing import Optional
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import set_seed
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from diffusers import (
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AutoencoderKL,
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DDPMScheduler,
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StableDiffusionInpaintPipeline,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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)
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from diffusers.optimization import get_scheduler
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from huggingface_hub import HfFolder, Repository, whoami
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from PIL import Image, ImageDraw
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from torch.utils.data import Dataset
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import CLIPTextModel, CLIPTokenizer
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logger = get_logger(__name__)
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def prepare_mask_and_masked_image(image, mask):
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image = np.array(image.convert("RGB"))
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
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mask = np.array(mask.convert("L"))
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mask = mask.astype(np.float32) / 255.0
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mask = mask[None, None]
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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mask = torch.from_numpy(mask)
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masked_image = image * (mask < 0.5)
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return mask, masked_image
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# generate random masks
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def random_mask(im_shape, ratio=1, mask_full_image=False):
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mask = Image.new("L", im_shape, 0)
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draw = ImageDraw.Draw(mask)
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size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio)))
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# use this to always mask the whole image
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if mask_full_image:
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size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio))
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limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2)
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center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1]))
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draw_type = random.randint(0, 1)
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if draw_type == 0 or mask_full_image:
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draw.rectangle(
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(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
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fill=255,
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)
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else:
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draw.ellipse(
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(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
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fill=255,
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)
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return mask
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--instance_data_dir",
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type=str,
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default=None,
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required=True,
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help="A folder containing the training data of instance images.",
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)
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parser.add_argument(
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"--class_data_dir",
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type=str,
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default=None,
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required=False,
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help="A folder containing the training data of class images.",
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)
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parser.add_argument(
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"--instance_prompt",
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type=str,
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default=None,
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help="The prompt with identifier specifying the instance",
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)
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parser.add_argument(
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"--class_prompt",
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type=str,
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default=None,
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help="The prompt to specify images in the same class as provided instance images.",
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)
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parser.add_argument(
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"--with_prior_preservation",
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default=False,
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action="store_true",
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help="Flag to add prior preservation loss.",
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)
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parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
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parser.add_argument(
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"--num_class_images",
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type=int,
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default=100,
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help=(
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"Minimal class images for prior preservation loss. If not have enough images, additional images will be"
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" sampled with class_prompt."
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),
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="text-inversion-model",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--resolution",
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type=int,
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default=512,
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help=(
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"The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"
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),
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)
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parser.add_argument(
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"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
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)
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parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
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parser.add_argument(
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"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument("--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.")
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parser.add_argument("--num_train_epochs", type=int, default=1)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--gradient_checkpointing",
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action="store_true",
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help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=5e-6,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
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parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default="no",
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU."
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),
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)
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parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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args = parser.parse_args()
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env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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if env_local_rank != -1 and env_local_rank != args.local_rank:
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args.local_rank = env_local_rank
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if args.instance_data_dir is None:
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raise ValueError("You must specify a train data directory.")
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if args.with_prior_preservation:
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if args.class_data_dir is None:
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raise ValueError("You must specify a data directory for class images.")
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if args.class_prompt is None:
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raise ValueError("You must specify prompt for class images.")
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return args
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class DreamBoothDataset(Dataset):
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"""
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A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
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It pre-processes the images and the tokenizes prompts.
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"""
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def __init__(
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self,
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instance_data_root,
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instance_prompt,
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tokenizer,
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class_data_root=None,
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class_prompt=None,
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size=512,
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center_crop=False,
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):
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self.size = size
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self.center_crop = center_crop
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self.tokenizer = tokenizer
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self.instance_data_root = Path(instance_data_root)
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if not self.instance_data_root.exists():
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raise ValueError("Instance images root doesn't exists.")
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self.instance_images_path = list(Path(instance_data_root).iterdir())
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self.num_instance_images = len(self.instance_images_path)
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self.instance_prompt = instance_prompt
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self._length = self.num_instance_images
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if class_data_root is not None:
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self.class_data_root = Path(class_data_root)
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self.class_data_root.mkdir(parents=True, exist_ok=True)
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self.class_images_path = list(self.class_data_root.iterdir())
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self.num_class_images = len(self.class_images_path)
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self._length = max(self.num_class_images, self.num_instance_images)
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self.class_prompt = class_prompt
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else:
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self.class_data_root = None
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self.image_transforms = transforms.Compose(
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[
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transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
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transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]),
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]
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)
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def __len__(self):
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return self._length
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def __getitem__(self, index):
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example = {}
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instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
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if not instance_image.mode == "RGB":
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instance_image = instance_image.convert("RGB")
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example["PIL_images"] = instance_image
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example["instance_images"] = self.image_transforms(instance_image)
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example["instance_prompt_ids"] = self.tokenizer(
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self.instance_prompt,
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padding="do_not_pad",
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truncation=True,
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max_length=self.tokenizer.model_max_length,
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).input_ids
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if self.class_data_root:
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class_image = Image.open(self.class_images_path[index % self.num_class_images])
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if not class_image.mode == "RGB":
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class_image = class_image.convert("RGB")
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example["class_images"] = self.image_transforms(class_image)
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example["class_PIL_images"] = class_image
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example["class_prompt_ids"] = self.tokenizer(
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self.class_prompt,
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padding="do_not_pad",
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truncation=True,
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max_length=self.tokenizer.model_max_length,
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).input_ids
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return example
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class PromptDataset(Dataset):
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"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
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def __init__(self, prompt, num_samples):
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self.prompt = prompt
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self.num_samples = num_samples
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def __len__(self):
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return self.num_samples
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def __getitem__(self, index):
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example = {}
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example["prompt"] = self.prompt
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example["index"] = index
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return example
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def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
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if token is None:
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token = HfFolder.get_token()
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if organization is None:
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username = whoami(token)["name"]
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return f"{username}/{model_id}"
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else:
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return f"{organization}/{model_id}"
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def main():
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args = parse_args()
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logging_dir = Path(args.output_dir, args.logging_dir)
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accelerator = Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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mixed_precision=args.mixed_precision,
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log_with="tensorboard",
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logging_dir=logging_dir,
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)
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# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
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# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
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# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
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if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
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raise ValueError(
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"Gradient accumulation is not supported when training the text encoder in distributed training. "
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"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
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)
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if args.seed is not None:
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set_seed(args.seed)
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if args.with_prior_preservation:
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class_images_dir = Path(args.class_data_dir)
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if not class_images_dir.exists():
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class_images_dir.mkdir(parents=True)
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cur_class_images = len(list(class_images_dir.iterdir()))
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if cur_class_images < args.num_class_images:
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torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
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pipeline = StableDiffusionInpaintPipeline.from_pretrained(
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args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None
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)
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pipeline.set_progress_bar_config(disable=True)
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num_new_images = args.num_class_images - cur_class_images
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logger.info(f"Number of class images to sample: {num_new_images}.")
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sample_dataset = PromptDataset(args.class_prompt, num_new_images)
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sample_dataloader = torch.utils.data.DataLoader(
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sample_dataset, batch_size=args.sample_batch_size, num_workers=1
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)
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sample_dataloader = accelerator.prepare(sample_dataloader)
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pipeline.to(accelerator.device)
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transform_to_pil = transforms.ToPILImage()
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for example in tqdm(
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sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
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):
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bsz = len(example["prompt"])
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fake_images = torch.rand((3, args.resolution, args.resolution))
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transform_to_pil = transforms.ToPILImage()
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fake_pil_images = transform_to_pil(fake_images)
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fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True)
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images = pipeline(prompt=example["prompt"], mask_image=fake_mask, image=fake_pil_images).images
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for i, image in enumerate(images):
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hash_image = hashlib.sha1(image.tobytes()).hexdigest()
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image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
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image.save(image_filename)
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|
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del pipeline
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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|
|
|
# Handle the repository creation
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|
if accelerator.is_main_process:
|
|
if args.push_to_hub:
|
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if args.hub_model_id is None:
|
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repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
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else:
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repo_name = args.hub_model_id
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repo = Repository(args.output_dir, clone_from=repo_name)
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|
|
|
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
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if "step_*" not in gitignore:
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gitignore.write("step_*\n")
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if "epoch_*" not in gitignore:
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gitignore.write("epoch_*\n")
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elif args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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|
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# Load the tokenizer
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if args.tokenizer_name:
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tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
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elif args.pretrained_model_name_or_path:
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tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
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# Load models and create wrapper for stable diffusion
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text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
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vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
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unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
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|
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vae.requires_grad_(False)
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if not args.train_text_encoder:
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text_encoder.requires_grad_(False)
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|
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if args.gradient_checkpointing:
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unet.enable_gradient_checkpointing()
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if args.train_text_encoder:
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text_encoder.gradient_checkpointing_enable()
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|
|
if args.scale_lr:
|
|
args.learning_rate = (
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args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
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)
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|
|
|
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
|
|
if args.use_8bit_adam:
|
|
try:
|
|
import bitsandbytes as bnb
|
|
except ImportError:
|
|
raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.")
|
|
|
|
optimizer_class = bnb.optim.AdamW8bit
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|
else:
|
|
optimizer_class = torch.optim.AdamW
|
|
|
|
params_to_optimize = (
|
|
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
|
|
)
|
|
optimizer = optimizer_class(
|
|
params_to_optimize,
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
|
|
train_dataset = DreamBoothDataset(
|
|
instance_data_root=args.instance_data_dir,
|
|
instance_prompt=args.instance_prompt,
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|
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
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|
class_prompt=args.class_prompt,
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|
tokenizer=tokenizer,
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|
size=args.resolution,
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|
center_crop=args.center_crop,
|
|
)
|
|
|
|
def collate_fn(examples):
|
|
image_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
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|
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
|
|
]
|
|
)
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|
input_ids = [example["instance_prompt_ids"] for example in examples]
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|
pixel_values = [example["instance_images"] for example in examples]
|
|
|
|
# Concat class and instance examples for prior preservation.
|
|
# We do this to avoid doing two forward passes.
|
|
if args.with_prior_preservation:
|
|
input_ids += [example["class_prompt_ids"] for example in examples]
|
|
pixel_values += [example["class_images"] for example in examples]
|
|
pior_pil = [example["class_PIL_images"] for example in examples]
|
|
|
|
masks = []
|
|
masked_images = []
|
|
for example in examples:
|
|
pil_image = example["PIL_images"]
|
|
# generate a random mask
|
|
mask = random_mask(pil_image.size, 1, False)
|
|
# apply transforms
|
|
mask = image_transforms(mask)
|
|
pil_image = image_transforms(pil_image)
|
|
# prepare mask and masked image
|
|
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
|
|
|
|
masks.append(mask)
|
|
masked_images.append(masked_image)
|
|
|
|
if args.with_prior_preservation:
|
|
for pil_image in pior_pil:
|
|
# generate a random mask
|
|
mask = random_mask(pil_image.size, 1, False)
|
|
# apply transforms
|
|
mask = image_transforms(mask)
|
|
pil_image = image_transforms(pil_image)
|
|
# prepare mask and masked image
|
|
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
|
|
|
|
masks.append(mask)
|
|
masked_images.append(masked_image)
|
|
|
|
pixel_values = torch.stack(pixel_values)
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
|
|
masks = torch.stack(masks)
|
|
masked_images = torch.stack(masked_images)
|
|
batch = {"input_ids": input_ids, "pixel_values": pixel_values, "masks": masks, "masked_images": masked_images}
|
|
return batch
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
|
|
)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
args.lr_scheduler,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
|
|
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
|
)
|
|
|
|
if args.train_text_encoder:
|
|
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
else:
|
|
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
unet, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
weight_dtype = torch.float32
|
|
if args.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
elif args.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
|
|
# Move text_encode and vae to gpu.
|
|
# For mixed precision training we cast the text_encoder and vae weights to half-precision
|
|
# as these models are only used for inference, keeping weights in full precision is not required.
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
if not args.train_text_encoder:
|
|
text_encoder.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if accelerator.is_main_process:
|
|
accelerator.init_trackers("dreambooth", config=vars(args))
|
|
|
|
# Train!
|
|
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
|
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
|
|
progress_bar.set_description("Steps")
|
|
global_step = 0
|
|
|
|
for epoch in range(args.num_train_epochs):
|
|
unet.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(unet):
|
|
# Convert images to latent space
|
|
|
|
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
latents = latents * 0.18215
|
|
|
|
# Convert masked images to latent space
|
|
masked_latents = vae.encode(
|
|
batch["masked_images"].reshape(batch["pixel_values"].shape).to(dtype=weight_dtype)
|
|
).latent_dist.sample()
|
|
masked_latents = masked_latents * 0.18215
|
|
|
|
masks = batch["masks"]
|
|
# resize the mask to latents shape as we concatenate the mask to the latents
|
|
mask = torch.stack(
|
|
[
|
|
torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))
|
|
for mask in masks
|
|
]
|
|
)
|
|
mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
bsz = latents.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
|
|
timesteps = timesteps.long()
|
|
|
|
# Add noise to the latents according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
|
|
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
|
|
|
# concatenate the noised latents with the mask and the masked latents
|
|
latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)
|
|
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
|
|
|
|
# Predict the noise residual
|
|
noise_pred = unet(latent_model_input, timesteps, encoder_hidden_states).sample
|
|
|
|
# Get the target for loss depending on the prediction type
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
if args.with_prior_preservation:
|
|
# Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
|
|
noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
|
|
target, target_prior = torch.chunk(target, 2, dim=0)
|
|
|
|
# Compute instance loss
|
|
loss = F.mse_loss(noise_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
|
|
|
|
# Compute prior loss
|
|
prior_loss = F.mse_loss(noise_pred_prior.float(), target_prior.float(), reduction="mean")
|
|
|
|
# Add the prior loss to the instance loss.
|
|
loss = loss + args.prior_loss_weight * prior_loss
|
|
else:
|
|
loss = F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
|
|
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = (
|
|
itertools.chain(unet.parameters(), text_encoder.parameters())
|
|
if args.train_text_encoder
|
|
else unet.parameters()
|
|
)
|
|
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
|
|
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
accelerator.log(logs, step=global_step)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
accelerator.wait_for_everyone()
|
|
|
|
# Create the pipeline using using the trained modules and save it.
|
|
if accelerator.is_main_process:
|
|
pipeline = StableDiffusionPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
unet=accelerator.unwrap_model(unet),
|
|
text_encoder=accelerator.unwrap_model(text_encoder),
|
|
)
|
|
pipeline.save_pretrained(args.output_dir)
|
|
|
|
if args.push_to_hub:
|
|
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|